import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SwiftFormerConfig,
    SwiftFormerForImageClassification,
    ViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE 		=      logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE 		=      torch.device("""cpu""")





def 			lowercase(	)		->	Any:







 '''simple docstring'''
 UpperCamelCase			  =      """http://images.cocodataset.org/val2017/000000039769.jpg"""
 UpperCamelCase			  =      Image.open(requests.get(UpperCamelCase_							,					stream=UpperCamelCase_       ).raw       )
 return im





def 			lowercase(	UpperCamelCase_       )		->	int:







 '''simple docstring'''
 if swiftformer_name == "swiftformer_xs":
  return torch.tensor([-2.1703E00, 2.1107E00, -2.0811E00, 8.8685E-01, 2.4360E-01]       )

 elif swiftformer_name == "swiftformer_s":
  return torch.tensor([3.9636E-01, 2.3478E-01, -1.6963E00, -1.7381E00, -8.6337E-01]       )

 elif swiftformer_name == "swiftformer_l1":
  return torch.tensor([-4.2768E-01, -4.7429E-01, -1.0897E00, -1.0248E00, 3.5523E-02]       )

 elif swiftformer_name == "swiftformer_l3":
  return torch.tensor([-2.5330E-01, 2.4211E-01, -6.0185E-01, -8.2789E-01, -6.0446E-02]       )





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )		->	List[str]:







 '''simple docstring'''
 UpperCamelCase			  =      dct.pop(UpperCamelCase_       )
 UpperCamelCase			  =      val





def 			lowercase(	UpperCamelCase_       )		->	Any:







 '''simple docstring'''
 UpperCamelCase			  =      []
 for k in state_dict.keys():
  UpperCamelCase			  =      k
  if ".pwconv" in k:
   UpperCamelCase			  =      k_new.replace(""".pwconv"""							,					""".point_wise_conv"""       )
  if ".dwconv" in k:
   UpperCamelCase			  =      k_new.replace(""".dwconv"""							,					""".depth_wise_conv"""       )
  if ".Proj." in k:
   UpperCamelCase			  =      k_new.replace(""".Proj."""							,					""".proj."""       )
  if "patch_embed" in k_new:
   UpperCamelCase			  =      k_new.replace("""patch_embed"""							,					"""swiftformer.patch_embed.patch_embedding"""       )
  if "network" in k_new:
   UpperCamelCase			  =      k_new.split("""."""       )
   if ls[2].isdigit():
    UpperCamelCase			  =      """swiftformer.encoder.network.""" + ls[1] + """.blocks.""" + ls[2] + """.""" + """.""".join(ls[3:]       )
   else:
    UpperCamelCase			  =      k_new.replace("""network"""							,					"""swiftformer.encoder.network"""       )
  rename_keys.append((k, k_new)       )
 return rename_keys





@torch.no_grad()
def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )		->	Tuple:







 '''simple docstring'''
 UpperCamelCase			  =      SwiftFormerConfig()

 # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
 UpperCamelCase			  =      1000
 UpperCamelCase			  =      """huggingface/label-files"""
 UpperCamelCase			  =      """imagenet-1k-id2label.json"""
 UpperCamelCase			  =      json.load(open(hf_hub_download(UpperCamelCase_							,					UpperCamelCase_							,					repo_type="""dataset"""       )							,					"""r"""       )       )
 UpperCamelCase			  =      {int(UpperCamelCase_       ): v for k, v in idalabel.items()}
 UpperCamelCase			  =      idalabel
 UpperCamelCase			  =      {v: k for k, v in idalabel.items()}

 # size of the architecture
 if swiftformer_name == "swiftformer_xs":
  UpperCamelCase			  =      [3, 3, 6, 4]
  UpperCamelCase			  =      [48, 56, 112, 220]

 elif swiftformer_name == "swiftformer_s":
  UpperCamelCase			  =      [3, 3, 9, 6]
  UpperCamelCase			  =      [48, 64, 168, 224]

 elif swiftformer_name == "swiftformer_l1":
  UpperCamelCase			  =      [4, 3, 10, 5]
  UpperCamelCase			  =      [48, 96, 192, 384]

 elif swiftformer_name == "swiftformer_l3":
  UpperCamelCase			  =      [4, 4, 12, 6]
  UpperCamelCase			  =      [64, 128, 320, 512]

 # load state_dict of original model, remove and rename some keys
 if original_ckpt:
  if original_ckpt.startswith("""https"""       ):
   UpperCamelCase			  =      torch.hub.load_state_dict_from_url(UpperCamelCase_							,					map_location="""cpu"""							,					check_hash=UpperCamelCase_       )
  else:
   UpperCamelCase			  =      torch.load(UpperCamelCase_							,					map_location="""cpu"""       )
 UpperCamelCase			  =      checkpoint

 UpperCamelCase			  =      create_rename_keys(UpperCamelCase_       )
 for rename_key_src, rename_key_dest in rename_keys:
  rename_key(UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )

 # load HuggingFace model
 UpperCamelCase			  =      SwiftFormerForImageClassification(UpperCamelCase_       ).eval()
 hf_model.load_state_dict(UpperCamelCase_       )

 # prepare test inputs
 UpperCamelCase			  =      prepare_img()
 UpperCamelCase			  =      ViTImageProcessor.from_pretrained("""preprocessor_config"""       )
 UpperCamelCase			  =      processor(images=UpperCamelCase_							,					return_tensors="""pt"""       )

 # compare outputs from both models
 UpperCamelCase			  =      get_expected_output(UpperCamelCase_       )
 UpperCamelCase			  =      hf_model(inputs["""pixel_values"""]       ).logits

 assert hf_logits.shape == torch.Size([1, 1000]       )
 assert torch.allclose(hf_logits[0, 0:5]							,					UpperCamelCase_							,					atol=1E-3       )

 Path(UpperCamelCase_       ).mkdir(exist_ok=UpperCamelCase_       )
 print(f"""Saving model {swiftformer_name} to {pytorch_dump_folder_path}"""       )
 hf_model.save_pretrained(UpperCamelCase_       )


if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE 		=      argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         """--swiftformer_name""",
         default="""swiftformer_xs""",
         choices=["""swiftformer_xs""", """swiftformer_s""", """swiftformer_l1""", """swiftformer_l3"""],
         type=str,
         help="""Name of the SwiftFormer model you'd like to convert.""",
     )
     parser.add_argument(
         """--pytorch_dump_folder_path""",
         default="""./converted_outputs/""",
         type=str,
         help="""Path to the output PyTorch model directory.""",
     )
     parser.add_argument("""--original_ckpt""", default=None, type=str, help="""Path to the original model checkpoint.""")

     _SCREAMING_SNAKE_CASE 		=      parser.parse_args()
     convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)

# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"

from pathlib import Path
import json
import tempfile

from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES

_SCREAMING_SNAKE_CASE 		=      """tiny-wmt19-en-ru"""

# Build

# borrowed from a test
_SCREAMING_SNAKE_CASE 		=      [
    """l""",
    """o""",
    """w""",
    """e""",
    """r""",
    """s""",
    """t""",
    """i""",
    """d""",
    """n""",
    """w</w>""",
    """r</w>""",
    """t</w>""",
    """lo""",
    """low""",
    """er</w>""",
    """low</w>""",
    """lowest</w>""",
    """newer</w>""",
    """wider</w>""",
    """<unk>""",
]
_SCREAMING_SNAKE_CASE 		=      dict(zip(vocab, range(len(vocab))))
_SCREAMING_SNAKE_CASE 		=      ["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]

with tempfile.TemporaryDirectory() as tmpdirname:
     _SCREAMING_SNAKE_CASE 		=      Path(tmpdirname)
     _SCREAMING_SNAKE_CASE 		=      build_dir / VOCAB_FILES_NAMES["""src_vocab_file"""]
     _SCREAMING_SNAKE_CASE 		=      build_dir / VOCAB_FILES_NAMES["""tgt_vocab_file"""]
     _SCREAMING_SNAKE_CASE 		=      build_dir / VOCAB_FILES_NAMES["""merges_file"""]
     with open(src_vocab_file, """w""") as fp:
          fp.write(json.dumps(vocab_tokens))
     with open(tgt_vocab_file, """w""") as fp:
          fp.write(json.dumps(vocab_tokens))
     with open(merges_file, """w""") as fp:
          fp.write("""\n""".join(merges))

     _SCREAMING_SNAKE_CASE 		=      FSMTTokenizer(
         langs=["""en""", """ru"""],
         src_vocab_size=len(vocab),
         tgt_vocab_size=len(vocab),
         src_vocab_file=src_vocab_file,
         tgt_vocab_file=tgt_vocab_file,
         merges_file=merges_file,
     )

_SCREAMING_SNAKE_CASE 		=      FSMTConfig(
    langs=["""ru""", """en"""],
    src_vocab_size=1_0_0_0,
    tgt_vocab_size=1_0_0_0,
    d_model=4,
    encoder_layers=1,
    decoder_layers=1,
    encoder_ffn_dim=4,
    decoder_ffn_dim=4,
    encoder_attention_heads=1,
    decoder_attention_heads=1,
)

_SCREAMING_SNAKE_CASE 		=      FSMTForConditionalGeneration(config)
print(F'''num of params {tiny_model.num_parameters()}''')

# Test
_SCREAMING_SNAKE_CASE 		=      tokenizer(["""Making tiny model"""], return_tensors="""pt""")
_SCREAMING_SNAKE_CASE 		=      tiny_model(**batch)

print("""test output:""", len(outputs.logits[0]))

# Save
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(F'''Generated {mname_tiny}''')

# Upload
# transformers-cli upload tiny-wmt19-en-ru
